Abstract
We propose a novel variance component approach for the analysis of next-generation sequencing data. Our method is based on the detection of the proportion of the trait phenotypic variance that can be explained by the introduction of a new variance component that accounts for the local gene-specific departure of the empirical kinship relationship matrix, estimated from single-nucleotide polymorphism (SNP) genotypes, from their theoretical expectation based on the genealogical information in the pedigree. We tested our method with simulated phenotypes and imputed SNP genotypes from the Genetic Analysis Workshop 18 data set. We observed considerable variation in the differences between theoretical and gene-specific kinship estimates that proved to be informative for our test and allowed us to detect the MAP4 causal gene at a genome-wide significance level. The distribution of our test statistic show no inflation under the null hypothesis and results from a random set of genes suggest that the detection of MAP4 is both sensitive and specific. The use of 2 different strategies for the selection of the SNPs used to derive the gene-specific empirical kinship relationship matrices provides us with suggestive evidence that our method is performing as an empirical test of linkage.
| Original language | English (US) |
|---|---|
| Article number | S49 |
| Journal | BMC Proceedings |
| Volume | 8 |
| DOIs | |
| State | Published - Jun 17 2014 |
| Externally published | Yes |
Fingerprint
ASJC Scopus subject areas
- Medicine(all)
- Biochemistry, Genetics and Molecular Biology(all)
Cite this
A variance component-based gene burden test. / Peralta, Juan M.; Almeida, Marcio; Kent, Jack W.; Blangero, John.
In: BMC Proceedings, Vol. 8, S49, 17.06.2014.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A variance component-based gene burden test
AU - Peralta, Juan M.
AU - Almeida, Marcio
AU - Kent, Jack W.
AU - Blangero, John
PY - 2014/6/17
Y1 - 2014/6/17
N2 - We propose a novel variance component approach for the analysis of next-generation sequencing data. Our method is based on the detection of the proportion of the trait phenotypic variance that can be explained by the introduction of a new variance component that accounts for the local gene-specific departure of the empirical kinship relationship matrix, estimated from single-nucleotide polymorphism (SNP) genotypes, from their theoretical expectation based on the genealogical information in the pedigree. We tested our method with simulated phenotypes and imputed SNP genotypes from the Genetic Analysis Workshop 18 data set. We observed considerable variation in the differences between theoretical and gene-specific kinship estimates that proved to be informative for our test and allowed us to detect the MAP4 causal gene at a genome-wide significance level. The distribution of our test statistic show no inflation under the null hypothesis and results from a random set of genes suggest that the detection of MAP4 is both sensitive and specific. The use of 2 different strategies for the selection of the SNPs used to derive the gene-specific empirical kinship relationship matrices provides us with suggestive evidence that our method is performing as an empirical test of linkage.
AB - We propose a novel variance component approach for the analysis of next-generation sequencing data. Our method is based on the detection of the proportion of the trait phenotypic variance that can be explained by the introduction of a new variance component that accounts for the local gene-specific departure of the empirical kinship relationship matrix, estimated from single-nucleotide polymorphism (SNP) genotypes, from their theoretical expectation based on the genealogical information in the pedigree. We tested our method with simulated phenotypes and imputed SNP genotypes from the Genetic Analysis Workshop 18 data set. We observed considerable variation in the differences between theoretical and gene-specific kinship estimates that proved to be informative for our test and allowed us to detect the MAP4 causal gene at a genome-wide significance level. The distribution of our test statistic show no inflation under the null hypothesis and results from a random set of genes suggest that the detection of MAP4 is both sensitive and specific. The use of 2 different strategies for the selection of the SNPs used to derive the gene-specific empirical kinship relationship matrices provides us with suggestive evidence that our method is performing as an empirical test of linkage.
UR - http://www.scopus.com/inward/record.url?scp=85018193251&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018193251&partnerID=8YFLogxK
U2 - 10.1186/1753-6561-8-S1-S49
DO - 10.1186/1753-6561-8-S1-S49
M3 - Article
VL - 8
JO - BMC Proceedings
JF - BMC Proceedings
SN - 1753-6561
M1 - S49
ER -